| Hyperparameters and tuning strategies for random forest P Probst, MN Wright, AL Boulesteix Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, e1301, 2018 | 1013 | 2018 |
| Tunability: importance of hyperparameters of machine learning algorithms P Probst, B Bischl, AL Boulesteix Journal of Machine Learning Research 20 (53), 1-32, 2018 | 548 | 2018 |
| Random forest versus logistic regression: a large-scale benchmark experiment R Couronné, P Probst, AL Boulesteix BMC bioinformatics 19, 1-14, 2018 | 517 | 2018 |
| To Tune or Not to Tune the Number of Trees in Random Forest. P Probst, AL Boulesteix Journal of Machine Learning Research 18 (181), 1-18, 2018 | 397 | 2018 |
| Ranger: A fast implementation of random forests MN Wright, S Wager, P Probst R package version 0.12 1, 2020 | 101* | 2020 |
| Large-scale benchmark study of survival prediction methods using multi-omics data M Herrmann, P Probst, R Hornung, V Jurinovic, AL Boulesteix Briefings in bioinformatics 22 (3), bbaa167, 2021 | 51 | 2021 |
| Quality of life up to 10 years after traumatic brain injury: a cross-sectional analysis K Rauen, L Reichelt, P Probst, B Schäpers, F Müller, K Jahn, N Plesnila Health and quality of life outcomes 18, 1-12, 2020 | 34 | 2020 |
| Learning multiple defaults for machine learning algorithms F Pfisterer, JN van Rijn, P Probst, AC Müller, B Bischl Proceedings of the Genetic and Evolutionary Computation Conference Companion …, 2021 | 30 | 2021 |
| Multilabel classification with R package mlr P Probst, Q Au, G Casalicchio, C Stachl, B Bischl The R Journal 9 (1), 352--369, 2017 | 25 | 2017 |
| ranger: A Fast Implementation of Random Forests (2015) MN Wright, S Wager, P Probst R package, 2016 | 24 | 2016 |
| Quality of life after traumatic brain injury: a cross-sectional analysis uncovers age-and sex-related differences over the adult life span K Rauen, CB Späni, MC Tartaglia, MT Ferretti, L Reichelt, P Probst, ... GeroScience 43, 263-278, 2021 | 16 | 2021 |
| Package ‘ranger’ MN Wright, S Wager, P Probst, MMN Wright Version 0.11 2, 2019 | 15 | 2019 |
| Decompressive craniectomy is associated with good quality of life up to 10 years after rehabilitation from traumatic brain injury K Rauen, L Reichelt, P Probst, B Schäpers, F Müller, K Jahn, N Plesnila Critical care medicine 48 (8), 1157-1164, 2020 | 14 | 2020 |
| Making complex prediction rules applicable for readers: current practice in random forest literature and recommendations AL Boulesteix, S Janitza, R Hornung, P Probst, H Busen, A Hapfelmeier Biometrical Journal 61 (5), 1314-1328, 2019 | 14 | 2019 |
| tscount: Analysis of count time series T Liboschik, R Fried, K Fokianos, P Probst R package version 1 (0), 2016 | 14 | 2016 |
| Automatic exploration of machine learning experiments on openml D Kühn, P Probst, J Thomas, B Bischl arXiv preprint arXiv:1806.10961, 2018 | 12 | 2018 |
| Asthma features in severe COPD: identifying treatable traits S Matthes, J Stadler, J Barton, G Leuschner, D Munker, P Arnold, ... Respiratory Medicine 145, 89-94, 2018 | 10 | 2018 |
| Modern total knee arthroplasty (TKA): with or without a tourniquet? S Nicolaiciuc, P Probst, R von Eisenhart-Rothe, R Burgkart, R Hube Surgical Technology International 35, 336-340, 2019 | 8 | 2019 |
| Hyperparameters, tuning and meta-learning for random forest and other machine learning algorithms P Probst lmu, 2019 | 8 | 2019 |
| mlr Tutorial J Schiffner, B Bischl, M Lang, J Richter, ZM Jones, P Probst, F Pfisterer, ... arXiv preprint arXiv:1609.06146, 2016 | 8 | 2016 |
Marvin N. WrightLeibniz Institute for Prevention Research and Epidemiology – BIPS & University of BremenVerified email at leibniz-bips.de
